Hands-on Exercise 3

Part 1: Programming Interactive Data Visualisation with R

1.1 Getting Started

To begin this exercise, load the necessary R packages that support interactive and linked visualisations.

pacman::p_load(ggiraph, plotly, 
               patchwork, DT, tidyverse) 

1.2 Importing Data

We’ll work with a dataset containing students’ exam scores. This data will be used throughout the exercise to create various visualisations.

exam_data <- read_csv("Data/Exam_data.csv")

Rows: 322 Columns: 7
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (4): ID, CLASS, GENDER, RACE
dbl (3): ENGLISH, MATHS, SCIENCE

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

1.3 Interactive Visualisation with ggiraph

1.3.1 Dot Plot with Tooltips

This plot introduces basic interactivity where hovering over dots reveals student IDs.

p <- ggplot(data=exam_data, 
       aes(x = MATHS)) +
  geom_dotplot_interactive(
    aes(tooltip = ID),
    stackgroups = TRUE, 
    binwidth = 1, 
    method = "histodot") +
  scale_y_continuous(NULL, 
                     breaks = NULL)
girafe(
  ggobj = p,
  width_svg = 6,
  height_svg = 6*0.618
)

1.3.2 Displaying Multiple Tooltip Fields

Add richer information to tooltips by concatenating ID and class.

exam_data$tooltip <- c(paste0(     
  "Name = ", exam_data$ID,         
  "\n Class = ", exam_data$CLASS)) 

p <- ggplot(data=exam_data, 
       aes(x = MATHS)) +
  geom_dotplot_interactive(
    aes(tooltip = exam_data$tooltip), 
    stackgroups = TRUE,
    binwidth = 1,
    method = "histodot") +
  scale_y_continuous(NULL,               
                     breaks = NULL)
girafe(
  ggobj = p,
  width_svg = 8,
  height_svg = 8*0.618
)

1.3.3 Customising Tooltip style

Modify the tooltip appearance using inline CSS.

tooltip_css <- "background-color:white; #<<
font-style:bold; color:black;" #<<

p <- ggplot(data=exam_data, 
       aes(x = MATHS)) +
  geom_dotplot_interactive(              
    aes(tooltip = ID),                   
    stackgroups = TRUE,                  
    binwidth = 1,                        
    method = "histodot") +               
  scale_y_continuous(NULL,               
                     breaks = NULL)
girafe(                                  
  ggobj = p,                             
  width_svg = 6,                         
  height_svg = 6*0.618,
  options = list(    #<<
    opts_tooltip(    #<<
      css = tooltip_css)) #<<
)                                        

1.3.4 Displaying Summary Statistics in Tooltip

Show aggregate values like mean and SEM with hover feedback.

tooltip <- function(y, ymax, accuracy = .01) {
  mean <- scales::number(y, accuracy = accuracy)
  sem <- scales::number(ymax - y, accuracy = accuracy)
  paste("Mean maths scores:", mean, "+/-", sem)
}

gg_point <- ggplot(data=exam_data, 
                   aes(x = RACE),
) +
  stat_summary(aes(y = MATHS, 
                   tooltip = after_stat(  
                     tooltip(y, ymax))),  
    fun.data = "mean_se", 
    geom = GeomInteractiveCol,  
    fill = "light blue"
  ) +
  stat_summary(aes(y = MATHS),
    fun.data = mean_se,
    geom = "errorbar", width = 0.2, size = 0.2
  )

Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
ℹ Please use `linewidth` instead.

girafe(ggobj = gg_point,
       width_svg = 8,
       height_svg = 8*0.618)

Things to learn from the code chunk above

• Replace the size argument with linewidth ingeom = "errorbar"layer to resolve the warning.
• Explanation
  • size used to control the thickness of lines in earlier ggplot2 versions.
  • Since ggplot2 v3.4.0, size is reserved for point sizes, and linewidth should be used for line thickness to improve clarity and consistency.

Here is the corrected version of the code:

tooltip <- function(y, ymax, accuracy = .01) {
  mean <- scales::number(y, accuracy = accuracy)
  sem <- scales::number(ymax - y, accuracy = accuracy)
  paste("Mean maths scores:", mean, "+/-", sem)
}

gg_point <- ggplot(data=exam_data, 
                   aes(x = RACE),
) +
  stat_summary(aes(y = MATHS, 
                   tooltip = after_stat(  
                     tooltip(y, ymax))),  
    fun.data = "mean_se", 
    geom = GeomInteractiveCol,  
    fill = "light blue"
  ) +
  stat_summary(aes(y = MATHS),
    fun.data = mean_se,
    geom = "errorbar", width = 0.2, linewidth = 0.2
)

girafe(ggobj = gg_point,
       width_svg = 8,
       height_svg = 8*0.618)

1.3.5 Hover Effect with data_id

Implement visual effects such as highlighting and opacity change on hover.

p <- ggplot(data=exam_data, 
       aes(x = MATHS)) +
  geom_dotplot_interactive(           
    aes(data_id = CLASS),             
    stackgroups = TRUE,               
    binwidth = 1,                        
    method = "histodot") +               
  scale_y_continuous(NULL,               
                     breaks = NULL)
girafe(                                  
  ggobj = p,                             
  width_svg = 6,                         
  height_svg = 6*0.618                      
)                                        

1.3.6 Styling the Hover Effect

Implement visual effects such as highlighting and opacity change on hover.

p <- ggplot(data=exam_data, 
       aes(x = MATHS)) +
  geom_dotplot_interactive(              
    aes(data_id = CLASS),              
    stackgroups = TRUE,                  
    binwidth = 1,                        
    method = "histodot") +               
  scale_y_continuous(NULL,               
                     breaks = NULL)
girafe(                                  
  ggobj = p,                             
  width_svg = 6,                         
  height_svg = 6*0.618,
  options = list(                        
    opts_hover(css = "fill: #202020;"),  
    opts_hover_inv(css = "opacity:0.2;") 
  )                                        
)                                        

1.3.7 Tooltip + Hover Integration

Implement visual effects such as highlighting and opacity change on hover.

p <- ggplot(data=exam_data, 
       aes(x = MATHS)) +
  geom_dotplot_interactive(              
    aes(tooltip = CLASS, 
        data_id = CLASS),              
    stackgroups = TRUE,                  
    binwidth = 1,                        
    method = "histodot") +               
  scale_y_continuous(NULL,               
                     breaks = NULL)
girafe(                                  
  ggobj = p,                             
  width_svg = 6,                         
  height_svg = 6*0.618,
  options = list(                        
    opts_hover(css = "fill: #202020;"),  
    opts_hover_inv(css = "opacity:0.2;") 
  )                                        
)                                        

1.3.8 Adding Click Actions with onclick

Clicking a dot opens a URL with more student information.

exam_data$onclick <- sprintf("window.open(\"%s%s\")",
"https://www.moe.gov.sg/schoolfinder?journey=Primary%20school",
as.character(exam_data$ID))

p <- ggplot(data=exam_data, 
       aes(x = MATHS)) +
  geom_dotplot_interactive(              
    aes(onclick = onclick),              
    stackgroups = TRUE,                  
    binwidth = 1,                        
    method = "histodot") +               
  scale_y_continuous(NULL,               
                     breaks = NULL)
girafe(                                  
  ggobj = p,                             
  width_svg = 6,                         
  height_svg = 6*0.618)                                        

1.3.9 Coordinated Views

Synchronise hover effects across multiple views using shared IDs.

p1 <- ggplot(data=exam_data, 
       aes(x = MATHS)) +
  geom_dotplot_interactive(              
    aes(data_id = ID),              
    stackgroups = TRUE,                  
    binwidth = 1,                        
    method = "histodot") +  
  coord_cartesian(xlim=c(0,100)) + 
  scale_y_continuous(NULL,               
                     breaks = NULL)

p2 <- ggplot(data=exam_data, 
       aes(x = ENGLISH)) +
  geom_dotplot_interactive(              
    aes(data_id = ID),              
    stackgroups = TRUE,                  
    binwidth = 1,                        
    method = "histodot") + 
  coord_cartesian(xlim=c(0,100)) + 
  scale_y_continuous(NULL,               
                     breaks = NULL)

girafe(code = print(p1 + p2), 
       width_svg = 6,
       height_svg = 3,
       options = list(
         opts_hover(css = "fill: #202020;"),
         opts_hover_inv(css = "opacity:0.2;")
         )
       ) 

1.4 Interactive Visualisation with Plotly

1.4.1 Scatter Plot

plot_ly(data = exam_data, 
             x = ~MATHS, 
             y = ~ENGLISH)

No trace type specified:
  Based on info supplied, a 'scatter' trace seems appropriate.
  Read more about this trace type -> https://plotly.com/r/reference/#scatter

No scatter mode specifed:
  Setting the mode to markers
  Read more about this attribute -> https://plotly.com/r/reference/#scatter-mode

Things to learn from the code chunk above

• “No trace type specified” mean we didn’t specify what kind of plot we want, but since we gave x and y, it will assume we want a scatter plot. By default, it chooses type = 'scatter'.
  
• “No scatter mode specified: Setting the mode to markers”means a scatter plot can have different “modes”, such as:
  • mode = ‘markers’ → only dots
  • mode = ‘lines’ → connected lines
  • mode = ‘lines+markers’ → both dots and lines
  • Since we didn’t specify a mode, Plotly chose: mode = 'markers' (i.e. dots only)

If we want to be more explicit, here is the code:

plot_ly(data = exam_data, 
        x = ~MATHS, 
        y = ~ENGLISH, 
        type = 'scatter', 
        mode = 'markers')

1.4.2 Encoding by Race

plot_ly(data = exam_data, 
        x = ~ENGLISH, 
        y = ~MATHS, 
        color = ~RACE)

No trace type specified:
  Based on info supplied, a 'scatter' trace seems appropriate.
  Read more about this trace type -> https://plotly.com/r/reference/#scatter

No scatter mode specifed:
  Setting the mode to markers
  Read more about this attribute -> https://plotly.com/r/reference/#scatter-mode

Similar to the note of previous code chunk, we can specify the trace type and mode to be more explicit:

plot_ly(data = exam_data, 
        x = ~ENGLISH, 
        y = ~MATHS, 
        color = ~RACE,
        type = 'scatter', 
        mode = 'markers')

1.4.3 ggplot Conversion

Convert existing ggplot2 objects into interactive plots.

p <- ggplot(data=exam_data, 
            aes(x = MATHS,
                y = ENGLISH)) +
  geom_point(size=1) +
  coord_cartesian(xlim=c(0,100),
                  ylim=c(0,100))
ggplotly(p)

1.4.4 Coordinated Linked Views

Highlight observations across multiple charts using highlight_key() and subplot().

d <- highlight_key(exam_data)
p1 <- ggplot(data=d, 
            aes(x = MATHS,
                y = ENGLISH)) +
  geom_point(size=1) +
  coord_cartesian(xlim=c(0,100),
                  ylim=c(0,100))

p2 <- ggplot(data=d, 
            aes(x = MATHS,
                y = SCIENCE)) +
  geom_point(size=1) +
  coord_cartesian(xlim=c(0,100),
                  ylim=c(0,100))
subplot(ggplotly(p1),
        ggplotly(p2))

1.5 Crosstalk and DT

1.5.1 Interactive Table

Render a searchable, sortable, compact datatable using DT.

DT::datatable(exam_data, class= "compact")

1.5.2 Linked Table and Plot

Link DT tables and plots interactively using the crosstalk framework.

d <- highlight_key(exam_data) 
p <- ggplot(d, 
            aes(ENGLISH, 
                MATHS)) + 
  geom_point(size=1) +
  coord_cartesian(xlim=c(0,100),
                  ylim=c(0,100))

gg <- highlight(ggplotly(p),        
                "plotly_selected")  

crosstalk::bscols(gg,               
                  DT::datatable(d), 
                  widths = 5)        

Setting the `off` event (i.e., 'plotly_deselect') to match the `on` event (i.e., 'plotly_selected'). You can change this default via the `highlight()` function.

Part 2: Programming Animated Statistical Graphics with R

2.1 Getting Started

Loading the R packages

pacman::p_load(readxl, gifski, gapminder,
               plotly, gganimate, tidyverse)

2.2 Importing the data

Load population data from Excel and prepare it for time-series animation.

col <- c("Country", "Continent")
globalPop <- read_xls("Data/GlobalPopulation.xls",
                      sheet="Data") %>%
  mutate(across(col, as.factor)) %>%
  mutate(Year = as.integer(Year))

Warning: There was 1 warning in `mutate()`.
ℹ In argument: `across(col, as.factor)`.
Caused by warning:
! Using an external vector in selections was deprecated in tidyselect 1.1.0.
ℹ Please use `all_of()` or `any_of()` instead.
  # Was:
  data %>% select(col)

  # Now:
  data %>% select(all_of(col))

See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.

Things to learn from the code chunk above

• We’re getting the above warning because we’re passing an external character vector col directly into across(), which uses tidyselect under the hood — and tidyselect no longer allows that syntax.
• Solution:
  • Use all_of() inside across()
  • Convert “Country” and “Continent” to factor (explicitly and correctly).
  • Convert “Year” column to integer.

Updated code chunk:

col <- c("Country", "Continent")

globalPop <- read_xls("Data/GlobalPopulation.xls",
                      sheet = "Data") %>%
  mutate(across(all_of(col), as.factor)) %>%
  mutate(Year = as.integer(Year))

2.3 Animated Visualisation with gganimate

2.3.1 Building a static population bubble plot

Create a base plot using geom_point() with aesthetic mappings.

ggplot(globalPop, aes(x = Old, y = Young, 
                      size = Population, 
                      colour = Country)) +
  geom_point(alpha = 0.7, 
             show.legend = FALSE) +
  scale_colour_manual(values = country_colors) +
  scale_size(range = c(2, 12)) +
  labs(title = 'Year: {frame_time}', 
       x = '% Aged', 
       y = '% Young') 

2.3.2 Animated Plot

Use transition_time() and ease_aes() to animate transitions over time.

ggplot(globalPop, aes(x = Old, y = Young, 
                      size = Population, 
                      colour = Country)) +
  geom_point(alpha = 0.7, 
             show.legend = FALSE) +
  scale_colour_manual(values = country_colors) +
  scale_size(range = c(2, 12)) +
  labs(title = 'Year: {frame_time}', 
       x = '% Aged', 
       y = '% Young') +
  transition_time(Year) +       
  ease_aes('linear')          

2.4 Animated Visualisation with plotly

2.4.1 Using ggplotly()

Convert animated ggplot objects to interactive plotly charts.

gg <- ggplot(globalPop, 
       aes(x = Old, 
           y = Young, 
           size = Population, 
           colour = Country)) +
  geom_point(aes(size = Population,
                 frame = Year),
             alpha = 0.7, 
             show.legend = FALSE) +
  scale_colour_manual(values = country_colors) +
  scale_size(range = c(2, 12)) +
  labs(x = '% Aged', 
       y = '% Young')

Warning in geom_point(aes(size = Population, frame = Year), alpha = 0.7, :
Ignoring unknown aesthetics: frame

ggplotly(gg)

Warning in p$x$data[firstFrame] <- p$x$frames[[1]]$data: number of items to
replace is not a multiple of replacement length

Things to learn from the code chunk above

• The first warning we’re getting: Ignoring unknown aesthetics: frame is because frame is not a valid aesthetic in ggplot2::geom_point() — it only works in Plotly when building animations.
• Solution: Use plotly::plot_ly() directly for animations instead of ggplot2::ggplot()
• The second warning”Warning in p$x$data[firstFrame] <- p$x$frames[[1]]$data: number of items to replace is not a multiple of replacement length” happens because ggplotly() tries to generate frames from ggplot object, but it has no built-in animation logic, and frame was misused in the aes() mapping. So plotly’s frame structure is broken or inconsistent.

We can use the code chunk below to get an interactive static bubble chart:

gg <- ggplot(globalPop, 
       aes(x = Old, 
           y = Young, 
           size = Population, 
           colour = Country)) +
  geom_point(alpha = 0.7, show.legend = FALSE) +
  scale_colour_manual(values = country_colors) +
  scale_size(range = c(2, 12)) +
  labs(x = '% Aged', 
       y = '% Young')

ggplotly(gg)

For animated version, we can switch to plotly::plot_ly()

plot_ly(globalPop,
        x = ~Old,
        y = ~Young,
        size = ~Population,
        color = ~Country,
        frame = ~Year,
        text = ~paste("Country:", Country, "<br>Year:", Year, "<br>Pop:", Population),
        type = 'scatter',
        mode = 'markers',
        sizes = c(10, 80)) %>%
  layout(xaxis = list(title = "% Aged"),
         yaxis = list(title = "% Young"))

Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
Returning the palette you asked for with that many colors
Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
Returning the palette you asked for with that many colors

Warning in p$x$data[firstFrame] <- p$x$frames[[1]]$data: number of items to
replace is not a multiple of replacement length

2.4.2 Fix Display Issues

Fix layout issues and hide unnecessary legends to clean up the output.

gg <- ggplot(globalPop, 
       aes(x = Old, 
           y = Young, 
           size = Population, 
           colour = Country)) +
  geom_point(aes(size = Population,
                 frame = Year),
             alpha = 0.7) +
  scale_colour_manual(values = country_colors) +
  scale_size(range = c(2, 12)) +
  labs(x = '% Aged', 
       y = '% Young') + 
  theme(legend.position='none')

Warning in geom_point(aes(size = Population, frame = Year), alpha = 0.7):
Ignoring unknown aesthetics: frame

ggplotly(gg)

Warning in p$x$data[firstFrame] <- p$x$frames[[1]]$data: number of items to
replace is not a multiple of replacement length

2.4.3 Directly with plot_ly()

Use plot_ly() directly to generate a bubble chart animated by year.

bp <- globalPop %>%
  plot_ly(x = ~Old, 
          y = ~Young, 
          size = ~Population, 
          color = ~Continent,
          sizes = c(2, 100),
          frame = ~Year, 
          text = ~Country, 
          hoverinfo = "text",
          type = 'scatter',
          mode = 'markers'
          ) %>%
  layout(showlegend = FALSE)
bp

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Warning: `line.width` does not currently support multiple values.
Warning: `line.width` does not currently support multiple values.
Warning: `line.width` does not currently support multiple values.
Warning: `line.width` does not currently support multiple values.
Warning: `line.width` does not currently support multiple values.
Warning: `line.width` does not currently support multiple values.
Warning: `line.width` does not currently support multiple values.
Warning: `line.width` does not currently support multiple values.
Warning: `line.width` does not currently support multiple values.
Warning: `line.width` does not currently support multiple values.
Warning: `line.width` does not currently support multiple values.
Warning: `line.width` does not currently support multiple values.
Warning: `line.width` does not currently support multiple values.
Warning: `line.width` does not currently support multiple values.
Warning: `line.width` does not currently support multiple values.
Warning: `line.width` does not currently support multiple values.
Warning: `line.width` does not currently support multiple values.
Warning: `line.width` does not currently support multiple values.
Warning: `line.width` does not currently support multiple values.
Warning: `line.width` does not currently support multiple values.
Warning: `line.width` does not currently support multiple values.
Warning: `line.width` does not currently support multiple values.
Warning: `line.width` does not currently support multiple values.
Warning: `line.width` does not currently support multiple values.
Warning: `line.width` does not currently support multiple values.
Warning: `line.width` does not currently support multiple values.
Warning: `line.width` does not currently support multiple values.
Warning: `line.width` does not currently support multiple values.
Warning: `line.width` does not currently support multiple values.
Warning: `line.width` does not currently support multiple values.
Warning: `line.width` does not currently support multiple values.
Warning: `line.width` does not currently support multiple values.

Things to learn from the code chunk above

• We’re seeing the repeated warning: Warning: line.width does not currently support multiple values.because Plotly is being asked to apply multiple line.width values (likely per-frame or per-point), but that property doesn’t support vectors — it expects a single value.
• When using plot_ly() with frame = ~Year (i.e., animation), each frame is a separate trace, and Plotly internally tries to apply styling (like line.width) across frames. If the dataset has more than one entry per frame, and it tries to assign a vector of widths, this warning appears.
• However — we’re not explicitly using line.width in the code, so it’s likely being set implicitly, possibly by an old or incompatible version of Plotly R or from system styles.

We can explicitly define both marker and symbol in the plot, and remove size = ~Population from the main aesthetic — instead, control it entirely inside marker.

library(plotly)

bp <- plot_ly(globalPop,
        x = ~Old,
        y = ~Young,
        frame = ~Year,
        text = ~paste("Country:", Country, "<br>Population:", Population),
        hoverinfo = "text",
        type = "scatter",
        mode = "markers",
        color = ~Continent,
        marker = list(
          sizemode = "area",
          sizeref = 2 * max(globalPop$Population, na.rm = TRUE) / (100^2),
          size = ~Population,
          line = list(width = 1)
        ),
        showlegend = FALSE
) %>%
  layout(
    xaxis = list(title = "% Aged"),
    yaxis = list(title = "% Young")
  )

bp

Summary

In this hands-on exercise, we learned how to create both interactive and animated data visualisations in R. Part 1 focused on interactive graphics using ggiraph, plotly, and DT for better data exploration and presentation. Part 2 introduced animated plots with gganimate and plotly to enhance time-based storytelling. These techniques are especially useful in building dashboards, reports, and applications where user engagement and clarity are key.